Overview

Dataset statistics

Number of variables14
Number of observations52560
Missing cells702
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 MiB
Average record size in memory112.0 B

Variable types

DateTime1
Numeric13

Alerts

Power (kW) is highly overall correlated with Rear bearing temperature (°C) and 6 other fieldsHigh correlation
Wind direction (°) is highly overall correlated with Nacelle position (°)High correlation
Nacelle position (°) is highly overall correlated with Wind direction (°)High correlation
blade_angle is highly overall correlated with Rear bearing temperature (°C)High correlation
Rear bearing temperature (°C) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Rotor speed (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Generator RPM (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Front bearing temperature (°C) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Tower Acceleration X (mm/ss) is highly overall correlated with Power (kW) and 4 other fieldsHigh correlation
Wind speed (m/s) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Tower Acceleration y (mm/ss) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
# Date and time has unique valuesUnique
blade_angle has 26645 (50.7%) zerosZeros
Rotor speed (RPM) has 1088 (2.1%) zerosZeros

Reproduction

Analysis started2023-07-08 11:56:05.055126
Analysis finished2023-07-08 11:56:22.663020
Duration17.61 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

# Date and time
Date

Distinct52560
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size410.8 KiB
Minimum2019-01-01 00:00:00
Maximum2019-12-31 23:50:00
2023-07-08T17:26:22.711659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:22.805546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Power (kW)
Real number (ℝ)

Distinct52433
Distinct (%)99.9%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean623.89283
Minimum-18.101918
Maximum2083.4319
Zeros4
Zeros (%)< 0.1%
Negative4711
Negative (%)9.0%
Memory size410.8 KiB
2023-07-08T17:26:23.021935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-18.101918
5-th percentile-0.94109403
Q1141.97562
median420.15825
Q3940.57816
95-th percentile1977.3979
Maximum2083.4319
Range2101.5338
Interquartile range (IQR)798.60254

Descriptive statistics

Standard deviation605.22744
Coefficient of variation (CV)0.97008237
Kurtosis-0.088307026
Mean623.89283
Median Absolute Deviation (MAD)337.60167
Skewness1.0114666
Sum32758117
Variance366300.25
MonotonicityNot monotonic
2023-07-08T17:26:23.113201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
< 0.1%
-1.907796025 2
 
< 0.1%
-0.8466645479 2
 
< 0.1%
-1.04886103 2
 
< 0.1%
-0.07468999922 2
 
< 0.1%
618.5131927 2
 
< 0.1%
-0.7015525192 2
 
< 0.1%
-1.150759578 2
 
< 0.1%
-0.829592523 2
 
< 0.1%
-1.09474206 2
 
< 0.1%
Other values (52423) 52484
99.9%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
-18.10191765 1
< 0.1%
-17.76586657 1
< 0.1%
-15.46290612 1
< 0.1%
-15.44427562 1
< 0.1%
-15.41809535 1
< 0.1%
-15.19667559 1
< 0.1%
-15.14486351 1
< 0.1%
-14.94853783 1
< 0.1%
-14.78527398 1
< 0.1%
-14.60291481 1
< 0.1%
ValueCountFrequency (%)
2083.431885 1
< 0.1%
2075.745907 1
< 0.1%
2074.148474 1
< 0.1%
2072.503571 1
< 0.1%
2072.254883 1
< 0.1%
2072.172217 1
< 0.1%
2071.283411 1
< 0.1%
2070.695972 1
< 0.1%
2070.622333 1
< 0.1%
2070.381189 1
< 0.1%

Wind direction (°)
Real number (ℝ)

Distinct52504
Distinct (%)> 99.9%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean203.34622
Minimum0.026458349
Maximum359.98658
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:26:23.210098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.026458349
5-th percentile29.877765
Q1144.96427
median219.53273
Q3267.42809
95-th percentile330.42288
Maximum359.98658
Range359.96013
Interquartile range (IQR)122.46382

Descriptive statistics

Standard deviation90.289252
Coefficient of variation (CV)0.44401737
Kurtosis-0.58478422
Mean203.34622
Median Absolute Deviation (MAD)56.26124
Skewness-0.52209732
Sum10676897
Variance8152.1491
MonotonicityNot monotonic
2023-07-08T17:26:23.306269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242.8060303 2
 
< 0.1%
303.7292175 2
 
< 0.1%
271.0650092 1
 
< 0.1%
268.0910556 1
 
< 0.1%
270.448367 1
 
< 0.1%
272.4037577 1
 
< 0.1%
269.6810921 1
 
< 0.1%
272.6402356 1
 
< 0.1%
272.8327807 1
 
< 0.1%
272.4587249 1
 
< 0.1%
Other values (52494) 52494
99.9%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
0.02645834891 1
< 0.1%
0.04472489103 1
< 0.1%
0.06803591322 1
< 0.1%
0.08938623847 1
< 0.1%
0.1044754942 1
< 0.1%
0.1410077214 1
< 0.1%
0.1659894735 1
< 0.1%
0.1709712356 1
< 0.1%
0.1737925139 1
< 0.1%
0.1842064852 1
< 0.1%
ValueCountFrequency (%)
359.9865849 1
< 0.1%
359.9435264 1
< 0.1%
359.9352173 1
< 0.1%
359.8913188 1
< 0.1%
359.8893881 1
< 0.1%
359.8874714 1
< 0.1%
359.8788618 1
< 0.1%
359.8734461 1
< 0.1%
359.8608892 1
< 0.1%
359.8607792 1
< 0.1%

Nacelle position (°)
Real number (ℝ)

Distinct11771
Distinct (%)22.4%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean205.05609
Minimum0.012661395
Maximum359.99337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:26:23.409350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.012661395
5-th percentile30.821178
Q1146.63467
median221.26907
Q3268.46487
95-th percentile331.02554
Maximum359.99337
Range359.98071
Interquartile range (IQR)121.8302

Descriptive statistics

Standard deviation90.193212
Coefficient of variation (CV)0.43984655
Kurtosis-0.57830251
Mean205.05609
Median Absolute Deviation (MAD)54.878754
Skewness-0.52456546
Sum10766675
Variance8134.8154
MonotonicityNot monotonic
2023-07-08T17:26:23.503979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
315.6596375 207
 
0.4%
243.2203674 185
 
0.4%
318.9523315 179
 
0.3%
118.0985947 179
 
0.3%
212.4890747 177
 
0.3%
258.5862732 175
 
0.3%
321.1474609 168
 
0.3%
239.9281921 168
 
0.3%
194.9275208 166
 
0.3%
247.6106262 165
 
0.3%
Other values (11761) 50737
96.5%
ValueCountFrequency (%)
0.01266139502 1
 
< 0.1%
0.03806725608 1
 
< 0.1%
0.2343323686 1
 
< 0.1%
0.3177888245 1
 
< 0.1%
0.4303837756 1
 
< 0.1%
0.5474435463 1
 
< 0.1%
0.6587524414 1
 
< 0.1%
0.6592690945 13
< 0.1%
0.6592712402 4
 
< 0.1%
0.6592719555 5
 
< 0.1%
ValueCountFrequency (%)
359.9933704 1
 
< 0.1%
359.9629803 1
 
< 0.1%
359.9526692 1
 
< 0.1%
359.9270516 1
 
< 0.1%
359.913663 1
 
< 0.1%
359.8868103 1
 
< 0.1%
359.7249534 1
 
< 0.1%
359.6493225 1
 
< 0.1%
359.5670471 1
 
< 0.1%
359.5627441 7
< 0.1%

blade_angle
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18930
Distinct (%)36.1%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4.8526014
Minimum0
Maximum92.489998
Zeros26645
Zeros (%)50.7%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:26:23.607863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.89683873
95-th percentile44.990002
Maximum92.489998
Range92.489998
Interquartile range (IQR)0.89683873

Descriptive statistics

Standard deviation13.694384
Coefficient of variation (CV)2.8220707
Kurtosis12.888988
Mean4.8526014
Median Absolute Deviation (MAD)0
Skewness3.4627591
Sum254790.69
Variance187.53616
MonotonicityNot monotonic
2023-07-08T17:26:23.702439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26645
50.7%
44.99333445 1565
 
3.0%
44.99000168 1215
 
2.3%
0.02449974513 388
 
0.7%
0.02466640632 272
 
0.5%
44.99666723 265
 
0.5%
1.49333334 192
 
0.4%
1.49000001 159
 
0.3%
0.0489995709 138
 
0.3%
89.98999786 115
 
0.2%
Other values (18920) 21552
41.0%
ValueCountFrequency (%)
0 26645
50.7%
0.0001666666552 2
 
< 0.1%
0.0001666666622 22
 
< 0.1%
0.0001754385918 2
 
< 0.1%
0.0001960784262 1
 
< 0.1%
0.0003333333104 2
 
< 0.1%
0.0003333333201 1
 
< 0.1%
0.0003333333244 13
 
< 0.1%
0.0003508771836 2
 
< 0.1%
0.0003921568474 1
 
< 0.1%
ValueCountFrequency (%)
92.48999786 26
< 0.1%
92.42666626 1
 
< 0.1%
92.41999817 4
 
< 0.1%
92.41666412 2
 
< 0.1%
92.20666504 1
 
< 0.1%
92.17499924 1
 
< 0.1%
91.96466479 1
 
< 0.1%
91.87666829 7
 
< 0.1%
91.87320931 1
 
< 0.1%
91.87216733 1
 
< 0.1%

Rear bearing temperature (°C)
Real number (ℝ)

Distinct36405
Distinct (%)69.3%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean65.555048
Minimum12.412499
Maximum77.177501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:26:23.797174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12.412499
5-th percentile45.72625
Q164.552499
median68.337171
Q370.502499
95-th percentile72.615468
Maximum77.177501
Range64.765001
Interquartile range (IQR)5.95

Descriptive statistics

Standard deviation8.5098161
Coefficient of variation (CV)0.12981176
Kurtosis6.6367741
Mean65.555048
Median Absolute Deviation (MAD)2.622829
Skewness-2.4115606
Sum3442033.4
Variance72.416971
MonotonicityNot monotonic
2023-07-08T17:26:23.893537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.41499977 12
 
< 0.1%
71.22250023 11
 
< 0.1%
69.5 10
 
< 0.1%
69.93499985 10
 
< 0.1%
71.5 10
 
< 0.1%
70.59250031 9
 
< 0.1%
68.18249969 9
 
< 0.1%
66.91000023 9
 
< 0.1%
69.46500015 9
 
< 0.1%
70.16499977 8
 
< 0.1%
Other values (36395) 52409
99.7%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
12.41249943 1
< 0.1%
12.53750038 1
< 0.1%
12.71500015 1
< 0.1%
12.72749996 1
< 0.1%
12.90499973 1
< 0.1%
13.06499958 1
< 0.1%
13.06750011 1
< 0.1%
13.08250046 1
< 0.1%
13.14000034 1
< 0.1%
13.21249962 1
< 0.1%
ValueCountFrequency (%)
77.17750053 1
< 0.1%
76.47500038 1
< 0.1%
76.23420996 1
< 0.1%
75.81499825 1
< 0.1%
75.56499901 1
< 0.1%
75.49473692 1
< 0.1%
75.49000015 1
< 0.1%
75.46500015 1
< 0.1%
75.43250046 1
< 0.1%
75.40000076 1
< 0.1%

Rotor speed (RPM)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51337
Distinct (%)97.8%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean10.612279
Minimum0
Maximum15.331223
Zeros1088
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:26:23.996585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.650427
Q18.4047252
median10.960105
Q313.929975
95-th percentile15.154102
Maximum15.331223
Range15.331223
Interquartile range (IQR)5.5252502

Descriptive statistics

Standard deviation3.9177866
Coefficient of variation (CV)0.36917485
Kurtosis0.95067028
Mean10.612279
Median Absolute Deviation (MAD)2.6862033
Skewness-1.067611
Sum557208.34
Variance15.349052
MonotonicityNot monotonic
2023-07-08T17:26:24.094862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1088
 
2.1%
0.01200000197 5
 
< 0.1%
0.01050000242 5
 
< 0.1%
0.02200000361 4
 
< 0.1%
0.01150000188 4
 
< 0.1%
0.02100000484 4
 
< 0.1%
0.04650000762 3
 
< 0.1%
0.0250000041 3
 
< 0.1%
8.139121056 2
 
< 0.1%
8.147932023 2
 
< 0.1%
Other values (51327) 51386
97.8%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
0 1088
2.1%
0.007287001703 1
 
< 0.1%
0.01050000242 5
 
< 0.1%
0.0110000018 2
 
< 0.1%
0.01110350224 1
 
< 0.1%
0.01150000188 4
 
< 0.1%
0.01200000197 5
 
< 0.1%
0.01250000205 2
 
< 0.1%
0.012738002 1
 
< 0.1%
0.01277777987 1
 
< 0.1%
ValueCountFrequency (%)
15.33122331 1
< 0.1%
15.31558657 1
< 0.1%
15.30546963 1
< 0.1%
15.30350143 1
< 0.1%
15.30209311 1
< 0.1%
15.28663841 1
< 0.1%
15.2852034 1
< 0.1%
15.2807765 1
< 0.1%
15.27978408 1
< 0.1%
15.27844669 1
< 0.1%

Generator RPM (RPM)
Real number (ℝ)

Distinct52489
Distinct (%)> 99.9%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1255.9311
Minimum-581.75966
Maximum1812.8119
Zeros0
Zeros (%)0.0%
Negative993
Negative (%)1.9%
Memory size410.8 KiB
2023-07-08T17:26:24.197033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-581.75966
5-th percentile75.994155
Q1995.40631
median1297.7823
Q31647.7726
95-th percentile1792.5689
Maximum1812.8119
Range2394.5715
Interquartile range (IQR)652.3663

Descriptive statistics

Standard deviation463.4671
Coefficient of variation (CV)0.36902271
Kurtosis0.9771205
Mean1255.9311
Median Absolute Deviation (MAD)317.40452
Skewness-1.0768523
Sum65943919
Variance214801.76
MonotonicityNot monotonic
2023-07-08T17:26:24.292341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-575.0001526 3
 
< 0.1%
965.3830566 2
 
< 0.1%
1036.99431 2
 
< 0.1%
1262.640696 2
 
< 0.1%
964.4343796 2
 
< 0.1%
965.4011707 2
 
< 0.1%
1793.076342 2
 
< 0.1%
1788.154541 2
 
< 0.1%
1384.389267 2
 
< 0.1%
965.6932354 2
 
< 0.1%
Other values (52479) 52485
99.9%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
-581.7596626 1
 
< 0.1%
-575.0001526 3
< 0.1%
-348.6255493 1
 
< 0.1%
-197.7348241 1
 
< 0.1%
-74.06507111 1
 
< 0.1%
-29.31118046 1
 
< 0.1%
-2.034090546 1
 
< 0.1%
-1.908019446 1
 
< 0.1%
-1.906374313 1
 
< 0.1%
-1.900188703 1
 
< 0.1%
ValueCountFrequency (%)
1812.811855 1
< 0.1%
1812.370949 1
< 0.1%
1810.411943 1
< 0.1%
1809.152588 1
< 0.1%
1808.97945 1
< 0.1%
1808.673414 1
< 0.1%
1807.400352 1
< 0.1%
1807.037683 1
< 0.1%
1806.530149 1
< 0.1%
1806.058472 1
< 0.1%

Nacelle ambient temperature (°C)
Real number (ℝ)

Distinct32582
Distinct (%)62.1%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean11.630808
Minimum-0.39000002
Maximum37.365
Zeros0
Zeros (%)0.0%
Negative31
Negative (%)0.1%
Memory size410.8 KiB
2023-07-08T17:26:24.387312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.39000002
5-th percentile3.4375001
Q17.2349999
median10.895
Q315.63
95-th percentile21.573388
Maximum37.365
Range37.755
Interquartile range (IQR)8.3950002

Descriptive statistics

Standard deviation5.7309683
Coefficient of variation (CV)0.49274035
Kurtosis0.11363323
Mean11.630808
Median Absolute Deviation (MAD)4.0917396
Skewness0.5382084
Sum610687.19
Variance32.843997
MonotonicityNot monotonic
2023-07-08T17:26:24.482884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.5 66
 
0.1%
7.800000191 65
 
0.1%
9.600000381 59
 
0.1%
7.699999809 59
 
0.1%
7.199999809 54
 
0.1%
6.400000095 51
 
0.1%
9 49
 
0.1%
8.600000381 49
 
0.1%
9.5 49
 
0.1%
7.099999905 47
 
0.1%
Other values (32572) 51958
98.9%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
-0.3900000155 2
< 0.1%
-0.3078947365 1
 
< 0.1%
-0.3000000119 1
 
< 0.1%
-0.2975000143 1
 
< 0.1%
-0.2899999917 1
 
< 0.1%
-0.2675000131 1
 
< 0.1%
-0.200000003 4
< 0.1%
-0.1899999976 1
 
< 0.1%
-0.1850000024 1
 
< 0.1%
-0.1625000089 1
 
< 0.1%
ValueCountFrequency (%)
37.36499996 1
< 0.1%
37.35500031 1
< 0.1%
37.20249901 1
< 0.1%
37.11578891 1
< 0.1%
37.0849987 1
< 0.1%
37.07499886 1
< 0.1%
37.00499935 1
< 0.1%
37.00249996 1
< 0.1%
36.8699995 1
< 0.1%
36.78999939 1
< 0.1%

Front bearing temperature (°C)
Real number (ℝ)

Distinct36713
Distinct (%)69.9%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean66.360425
Minimum13.1925
Maximum80.590001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:26:24.586703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13.1925
5-th percentile44.863125
Q163.455822
median70.725
Q372.562499
95-th percentile74.0525
Maximum80.590001
Range67.3975
Interquartile range (IQR)9.1066765

Descriptive statistics

Standard deviation9.6202024
Coefficient of variation (CV)0.14496897
Kurtosis3.6555537
Mean66.360425
Median Absolute Deviation (MAD)2.6800007
Skewness-1.8981549
Sum3484320.5
Variance92.548294
MonotonicityNot monotonic
2023-07-08T17:26:24.683105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.60000038 25
 
< 0.1%
28.5 11
 
< 0.1%
71.9375 11
 
< 0.1%
72.99499969 11
 
< 0.1%
72.62999992 11
 
< 0.1%
73.98499947 10
 
< 0.1%
73.25250015 10
 
< 0.1%
73.5 10
 
< 0.1%
73.59999847 10
 
< 0.1%
71.14000015 10
 
< 0.1%
Other values (36703) 52387
99.7%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
13.19250011 1
< 0.1%
13.21749973 1
< 0.1%
13.27000046 1
< 0.1%
13.35000038 1
< 0.1%
13.39500046 1
< 0.1%
13.40000057 1
< 0.1%
13.40999985 1
< 0.1%
13.5 1
< 0.1%
13.50500011 1
< 0.1%
13.57750034 1
< 0.1%
ValueCountFrequency (%)
80.59000053 1
< 0.1%
80.4125 1
< 0.1%
80.2299984 1
< 0.1%
80.14500084 1
< 0.1%
80.08249855 1
< 0.1%
80.04000015 1
< 0.1%
80.01249962 1
< 0.1%
79.87750244 1
< 0.1%
79.82368469 1
< 0.1%
79.77250099 1
< 0.1%

Tower Acceleration X (mm/ss)
Real number (ℝ)

Distinct52501
Distinct (%)> 99.9%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean50.374858
Minimum3.196888
Maximum227.0152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:26:24.905634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.196888
5-th percentile5.2051705
Q132.673609
median48.740711
Q365.338553
95-th percentile99.288176
Maximum227.0152
Range223.81831
Interquartile range (IQR)32.664944

Descriptive statistics

Standard deviation26.94448
Coefficient of variation (CV)0.53487952
Kurtosis0.76547996
Mean50.374858
Median Absolute Deviation (MAD)16.338651
Skewness0.58744465
Sum2644982.3
Variance726.005
MonotonicityNot monotonic
2023-07-08T17:26:24.995738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.57137299 2
 
< 0.1%
92.82759886 2
 
< 0.1%
38.48937225 2
 
< 0.1%
4.318139076 2
 
< 0.1%
4.509500331 2
 
< 0.1%
27.90045347 1
 
< 0.1%
31.67415433 1
 
< 0.1%
27.84218636 1
 
< 0.1%
25.71327167 1
 
< 0.1%
26.88801918 1
 
< 0.1%
Other values (52491) 52491
99.9%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
3.19688797 1
< 0.1%
3.224534035 1
< 0.1%
3.224674463 1
< 0.1%
3.263743639 1
< 0.1%
3.27657032 1
< 0.1%
3.328718907 1
< 0.1%
3.351088363 1
< 0.1%
3.386059976 1
< 0.1%
3.405959541 1
< 0.1%
3.416119808 1
< 0.1%
ValueCountFrequency (%)
227.0151958 1
< 0.1%
221.3224077 1
< 0.1%
219.597218 1
< 0.1%
209.9239513 1
< 0.1%
196.4120789 1
< 0.1%
193.2399105 1
< 0.1%
188.8620353 1
< 0.1%
187.3629291 1
< 0.1%
186.4765602 1
< 0.1%
184.8145145 1
< 0.1%

Wind speed (m/s)
Real number (ℝ)

Distinct52352
Distinct (%)99.7%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6.2295145
Minimum0.21558793
Maximum23.524443
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:26:25.089720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.21558793
5-th percentile2.2820761
Q14.3751556
median5.9959356
Q37.7534227
95-th percentile10.969502
Maximum23.524443
Range23.308855
Interquartile range (IQR)3.3782671

Descriptive statistics

Standard deviation2.7127211
Coefficient of variation (CV)0.43546268
Kurtosis1.0876886
Mean6.2295145
Median Absolute Deviation (MAD)1.6824226
Skewness0.7344376
Sum327086.89
Variance7.3588556
MonotonicityNot monotonic
2023-07-08T17:26:25.183982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.395249224 2
 
< 0.1%
4.240257275 2
 
< 0.1%
8.452181387 2
 
< 0.1%
6.69956584 2
 
< 0.1%
6.26154654 2
 
< 0.1%
10.41924951 2
 
< 0.1%
5.117131233 2
 
< 0.1%
7.160825372 2
 
< 0.1%
6.691621041 2
 
< 0.1%
3.807509708 2
 
< 0.1%
Other values (52342) 52486
99.9%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
0.2155879336 1
< 0.1%
0.2651472977 1
< 0.1%
0.2655750811 1
< 0.1%
0.2826189265 1
< 0.1%
0.2852438737 1
< 0.1%
0.2860688485 1
< 0.1%
0.3292876646 1
< 0.1%
0.342431426 1
< 0.1%
0.3494062863 1
< 0.1%
0.3628215102 1
< 0.1%
ValueCountFrequency (%)
23.52444288 1
< 0.1%
21.88654957 1
< 0.1%
20.76130929 1
< 0.1%
20.73014307 1
< 0.1%
20.66770301 1
< 0.1%
19.96327028 1
< 0.1%
19.72451978 1
< 0.1%
19.63717089 1
< 0.1%
19.57621913 1
< 0.1%
19.40031271 1
< 0.1%

Tower Acceleration y (mm/ss)
Real number (ℝ)

Distinct52499
Distinct (%)> 99.9%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean26.713831
Minimum1.7175852
Maximum181.36299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:26:25.277686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.7175852
5-th percentile5.3927499
Q116.588201
median24.047241
Q333.932122
95-th percentile53.948197
Maximum181.36299
Range179.6454
Interquartile range (IQR)17.343921

Descriptive statistics

Standard deviation15.643583
Coefficient of variation (CV)0.58559865
Kurtosis5.74967
Mean26.713831
Median Absolute Deviation (MAD)8.4437843
Skewness1.6865625
Sum1402636.4
Variance244.72169
MonotonicityNot monotonic
2023-07-08T17:26:25.374426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.32409668 2
 
< 0.1%
4.99539752 2
 
< 0.1%
12.86978357 2
 
< 0.1%
23.98501396 2
 
< 0.1%
22.51129723 2
 
< 0.1%
32.98952665 2
 
< 0.1%
19.94434166 2
 
< 0.1%
22.96952097 1
 
< 0.1%
15.88571837 1
 
< 0.1%
16.52234664 1
 
< 0.1%
Other values (52489) 52489
99.9%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
1.717585206 1
< 0.1%
3.170336723 1
< 0.1%
3.327203512 1
< 0.1%
3.385526657 1
< 0.1%
3.388614178 1
< 0.1%
3.504025942 1
< 0.1%
3.513316154 1
< 0.1%
3.543179544 1
< 0.1%
3.552834511 1
< 0.1%
3.570937872 1
< 0.1%
ValueCountFrequency (%)
181.362986 1
< 0.1%
179.4473062 1
< 0.1%
166.5267671 1
< 0.1%
166.4002805 1
< 0.1%
163.4649632 1
< 0.1%
158.9262373 1
< 0.1%
151.7713512 1
< 0.1%
145.3075642 1
< 0.1%
142.6911798 1
< 0.1%
142.1778874 1
< 0.1%

Metal particle count counter
Real number (ℝ)

Distinct35
Distinct (%)0.1%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean492.22898
Minimum475
Maximum510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:26:25.472430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum475
5-th percentile477
Q1486
median495
Q3499
95-th percentile507
Maximum510
Range35
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.4274246
Coefficient of variation (CV)0.019152518
Kurtosis-0.96013723
Mean492.22898
Median Absolute Deviation (MAD)6
Skewness-0.32519206
Sum25844975
Variance88.876334
MonotonicityIncreasing
2023-07-08T17:26:25.557806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
498 7866
15.0%
489 5257
 
10.0%
502 3743
 
7.1%
495 3703
 
7.0%
487 3407
 
6.5%
496 3062
 
5.8%
501 3045
 
5.8%
478 3000
 
5.7%
477 2581
 
4.9%
475 2241
 
4.3%
Other values (25) 14601
27.8%
ValueCountFrequency (%)
475 2241
4.3%
476 4
 
< 0.1%
477 2581
4.9%
478 3000
5.7%
479 971
 
1.8%
480 1613
3.1%
481 303
 
0.6%
482 58
 
0.1%
483 1296
2.5%
484 394
 
0.7%
ValueCountFrequency (%)
510 406
 
0.8%
509 1663
3.2%
508 106
 
0.2%
507 606
 
1.2%
505 457
 
0.9%
504 127
 
0.2%
503 414
 
0.8%
502 3743
7.1%
501 3045
5.8%
500 1923
3.7%

Interactions

2023-07-08T17:26:20.846900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:06.509710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:07.683331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:08.863227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:10.060382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:11.192062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:12.510760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:13.712687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:14.833579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:16.139012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:17.335310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:18.457379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:19.560818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:20.931214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:06.583730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:07.767318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:08.948871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:10.141287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:11.279146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:12.597274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:13.792882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:14.919376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:16.225833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:17.414972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:18.533746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:19.759940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:21.024905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:06.670995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:07.857850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:09.042770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:10.229465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:11.494573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:12.691017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:13.882482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:15.014573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:16.318933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:17.504710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:18.623210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:19.851169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:21.117363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:06.758749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:07.949478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:09.137013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:10.318826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:11.585037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:12.787383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:13.971972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:15.109190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:16.415057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:17.593819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:18.711304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:19.943764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:21.203429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:06.837594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:08.032976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:09.223002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:10.396921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:11.672343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:12.872365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:14.051275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:15.196706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:16.499096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:17.673289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:18.793029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:20.028436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:21.297703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:06.925795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:08.127352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:09.318818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:10.484213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:11.767565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:12.969687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:14.143557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:15.291642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:16.596067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:17.765441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:18.881081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:20.122441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:21.394481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:07.016049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:08.226126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:09.415023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:10.576200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:11.868381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:13.065226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:14.235504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:15.499790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:16.691618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:17.858667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:18.970900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:20.218549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:21.478603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:07.093850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:08.312816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:09.504636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:10.655378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:11.953642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:13.152695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:14.315564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:15.584615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:16.779588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:17.941210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:19.047726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:20.301931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:21.574342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:07.182728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:08.407910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:09.598994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:10.748729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:12.050025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:13.248415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:14.405161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:15.680356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:16.874743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:18.031070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:19.135673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:20.397798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:21.667488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:07.271022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:08.501922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:09.697672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:10.840896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:12.144861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:13.346231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:14.494916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:15.777831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:16.969393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:18.121494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:19.227035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:20.491099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:21.755041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:07.350770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:08.587826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:09.784483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:10.924348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:12.232647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:13.432226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:14.575989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:15.863317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:17.056020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:18.205525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:19.306262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:20.576656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:21.839999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:07.507493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:08.671016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:09.870027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:11.006531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:12.317989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:13.520200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:14.654690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:15.947693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:17.143258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:18.281786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:19.383726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:20.660142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:21.933106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:07.594276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:08.768581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:09.964935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:11.097378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:12.415182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:13.615384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:14.744562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:16.044231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:17.238579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:18.369118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:19.470702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:26:20.750273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-08T17:26:25.644004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Power (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
Power (kW)1.0000.0810.055-0.2170.6210.9940.994-0.1910.8660.5500.9830.7980.010
Wind direction (°)0.0811.0000.8970.0180.0370.0800.079-0.0370.0550.1790.0730.143-0.087
Nacelle position (°)0.0550.8971.0000.0360.0140.0540.053-0.0440.0310.1710.0520.129-0.081
blade_angle-0.2170.0180.0361.000-0.569-0.225-0.2260.112-0.358-0.015-0.202-0.035-0.057
Rear bearing temperature (°C)0.6210.0370.014-0.5691.0000.6210.6180.0690.8290.2860.6090.4160.068
Rotor speed (RPM)0.9940.0800.054-0.2250.6211.0000.999-0.1830.8680.5460.9780.7940.012
Generator RPM (RPM)0.9940.0790.053-0.2260.6180.9991.000-0.1960.8670.5450.9780.7940.009
Nacelle ambient temperature (°C)-0.191-0.037-0.0440.1120.069-0.183-0.1961.000-0.118-0.168-0.165-0.1580.146
Front bearing temperature (°C)0.8660.0550.031-0.3580.8290.8680.867-0.1181.0000.4440.8510.6560.049
Tower Acceleration X (mm/ss)0.5500.1790.171-0.0150.2860.5460.545-0.1680.4441.0000.5080.829-0.031
Wind speed (m/s)0.9830.0730.052-0.2020.6090.9780.978-0.1650.8510.5081.0000.7720.022
Tower Acceleration y (mm/ss)0.7980.1430.129-0.0350.4160.7940.794-0.1580.6560.8290.7721.000-0.019
Metal particle count counter0.010-0.087-0.081-0.0570.0680.0120.0090.1460.049-0.0310.022-0.0191.000

Missing values

2023-07-08T17:26:22.067072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T17:26:22.263577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-08T17:26:22.495736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
02019-01-01 00:00:00224.332199283.988098299.1961670.02578968.9175039.2102301096.7921149.00000069.12500049.3218805.08076717.686628475.0
12019-01-01 00:10:00141.542191283.394073299.1961670.07799867.1100018.4578291004.9431158.99500066.83250491.3953634.50278724.513630475.0
22019-01-01 00:20:00117.245667293.947510299.1961670.37049666.0325018.354611989.0166029.00000065.057503110.7178803.63755225.184965475.0
32019-01-01 00:30:00164.516144290.386078299.1961670.00000066.3675008.5004781006.4331058.99750065.26750294.6861724.40702327.299425475.0
42019-01-01 00:40:00102.386238305.513062299.1961670.37532965.8424998.299897983.1280529.01000064.590004109.6812823.76281130.749933475.0
52019-01-01 00:50:00126.354599317.534241316.3610840.24649765.5000008.339190988.1455089.01750063.86249970.5479354.15897221.600998475.0
62019-01-01 01:00:00192.371475319.007141324.4401550.00000066.4950038.7767401039.6817638.92250065.30500052.2128184.79097022.725674475.0
72019-01-01 01:10:0028.569798315.047363324.4401551.24017064.1950008.142853965.0731208.85500062.30749971.2470322.98443029.349892475.0
82019-01-01 01:20:0031.284975303.969971324.4401551.12250262.8050008.125270963.0329598.74000160.14250272.3152313.39579320.776865475.0
92019-01-01 01:30:00100.297920292.738159308.5388790.74433362.8525018.4842051006.8355718.71500060.01250169.4772343.90694923.247103475.0
# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
525502019-12-31 22:20:00198.937615102.715538112.6108170.00000067.9900018.9059361055.4318297.012568.18250056.8803994.71972727.769325510.0
525512019-12-31 22:30:00204.528512100.987804112.6108170.00000067.2700018.9127231057.4701656.970067.14000265.7382804.80203331.151040510.0
525522019-12-31 22:40:00185.73375292.190986107.4958860.02466666.7400018.7577281037.2003736.790066.36750141.8345184.49224319.057000510.0
525532019-12-31 22:50:00241.69925489.55330880.7816620.00000067.4849999.3441941107.7403586.637567.04000030.1631384.72763415.731838510.0
525542019-12-31 23:00:00226.41035892.90187480.7816620.00000067.6850019.1760391087.8417036.555067.43249940.8765324.61971217.205894510.0
525552019-12-31 23:10:00248.10376792.63238980.7816620.00000068.0824999.4183871116.4826556.455067.85750046.4322954.77634215.827043510.0
525562019-12-31 23:20:00253.25339093.57916480.7816620.00000068.2350009.4659111121.9157036.480068.13250049.6001684.92621622.027280510.0
525572019-12-31 23:30:00380.87201893.06821580.7816620.00000070.20750110.6464111262.3849496.455070.13000028.2571315.49571317.962244510.0
525582019-12-31 23:40:00337.753978103.20315192.9778850.00000070.28500110.2449241215.0458346.585070.62749841.7290705.18406818.515278510.0
525592019-12-31 23:50:00433.670004107.005053102.7328030.00000071.38000010.9938361303.0836476.690071.62750040.7711675.92755122.609447510.0